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The Giants Backfield Part I – What Does the Running Back Model Think of David Wilson?
Photo: Mark Runyon | Football Schedule

This is my third piece in a series where I run the numbers on some of the more convoluted backfield situations going into the 2013 season.  In the first, I advise some caution on assuming Zac Stacy can truly be a stud running back, partly because we have to keep an eye on Benny Cunningham.  In the second, I take a look at the mess that is the Green Bay backfield.  Here I’ll examine the David Wilson/Andre Brown battle for the Giants.

We all know the adage that fantasy success is “the intersection of talent and opportunity.” I like to use my running back model as a proxy for the “talent” part of the equation.  As I did with the Green Bay backfield, I looked at the Giants running back usage patterns (specifically around the goal line) for the “opportunity” part.  There is so much history with Tom Coughlin and so many different angles from which to look at his usage patterns that I decided to save it for another article and just focus on the talent part first.

If you want the full rundown of how I constructed my model and the theory behind it, you should check out my original piece where I use it to rank the 2013 rookies.  But for the sake of brevity, let’s jump right into the scores for the NYG backs.

To interpret the table: DR stands for Dominator Rating – a market share concept borrowed from Shawn Siegele’s work on wide receivers.  Upside Score is something I’ve been referring to in the past as “Draft Agnostic Model Score” – which doesn’t sound nearly as sexy.  Upside Score removes draft slot from the model and renormalizes the other factors.  More than being just a cool-sounding name, it really is what I think the number represents.  If two players have the same Model Score, but divergent Upside Scores I’ll target the higher Upside Score player ten times out of ten. Alternatively, if a player has a respectable Model Score but a really poor Upside Score it tells me that a lot of his model score is probably coming from his draft slot. In a situation like that, I think the player has a higher propensity to bust than someone with a good Model Score and a good Upside Score. Likewise, late- or un-drafted guys with really good Upside Scores are players I’d look to target late in drafts since they’re not very likely to see a lot of playing time early on, but they have the upside to break out if they’re given an opportunity.  Enough of my yammering – here are the Giants runners:

Player Model Score Upside Score College Year School Draft Slot Height Weight Forty Speed Score Rush DR Vert Broad Shuttle 3 Cone Agility
David Wilson 0.72 0.15 2011 Virginia Tech 32 70 206 4.49 101.4 0.49 41 132 4.12 7.09 11.21
Andre Brown 0.01 0.35 2008 North Carolina State 129 73 224 4.49 110.2 0.49 37 115 4.33 7.35 11.68
Ryan Torain -0.51 -0.76 2007 Arizona State 139 73 222 4.61 98.3 0.29 34 120
Da’Rel Scott -0.94 -0.17 2010 Maryland 221 71 211 4.34 118.9 0.32 34 117 4.20 7.15 11.35

I feel like starting with the Big Two in this backfield would be anticlimactic so here are my (quick) takes on Da’Rel Scott and Ryan Torain first:

Da’Rel Scott – Seventh round running backs generally don’t tend to have a lot of fantasy success, especially if they have low market shares of their college rushing stats.  Scott was basically in a 50/50 timeshare with Davin Meggett at Maryland, which renders both of them as average talents on my model.  His 118 Speed Score is very good and he did have a gorgeous preseason highlight run vs. the Pats.  Ultimately his lack of college dominance over a teammate who went undrafted is damning, and his draft slot implies no one thought much of his abilities.

Ryan Torain – Falling to the 5th round of the draft isn’t as bad as falling to the 7th on my model, but it’s still not good.  The below-average 98 Speed Score doesn’t help him either.  Torain’s full-season market share numbers weren’t good, but that’s partly because he only played in 6 of Arizona State’s 13 games in 2007.  His market share during the games he did play was respectable (51% of yards and 55% of TDs).  But even using those numbers he still scores a bad -0.11 (undraftable) on my Model Score, with a paltry 0.25 Upside Score (for reference, guys start to get interesting as they approach 1.00 on the Upside Score).

David Wilson – Wilson is one of the more polarizing players in dynasty today.  I won’t belabor the merits of Wilson’s talent here.  While I think the discussion is absolutely worthwhile (see this piece on not blindly following numbers, which I think is absolutely fantastic and exactly how I think about what we do at RotoViz – a MUST READ in my opinion), that’s not the purpose of my article.  I’m here to tell you what my model thinks.  While it likes Wilson the best of all the RBs on the roster, he doesn’t project as elite on the surface.  His draft slot helps him quite a bit.  Clearly the Giants liked him and expected other teams to feel the same way, or they wouldn’t have selected him in the first round.  Logically, you would think the Giants plan to get him a healthy workload sometime within the next season or two.  Wilson’s main downfall is his low Rushing DR (his market share of VT’s rushing yards and TDs).  HOWEVER, he falls prey to one of the flaws in my market-share-based model: the running QB problem.  Logan Thomas took a 30% share of the VT rushing market (18% of the yards and a whopping 42% of the TDs).

A quick digression to discuss the impact of rushing QBs on a model like mine:  I don’t think a suppressed market share of yards or touchdowns because of a running quarterback is an indictment of a running back’s talent/ability like a suppressed market share due to a competing running back is.  If a running back is losing significant carries/yards/TDs to another back on his team, it tells me he’s not all that talented – at least not relative to the other running back on his team.  (Further digression: It’s certainly possible that a team has two elite rushers on the roster at the same stage of development at the same time, in which case they cannibalize each other’s market share and trick the model into thinking neither is any good.  But personally, I think that scenario is highly unlikely.  Even if both rushers are truly elite, usually one of the two will be a little older or more experienced and therefore still get the bulk of the workload.  So let’s throw that scenario out for the moment.)  Teams are always grooming their next starter, but usually the way that player manifests himself in the stats is as a lesser share of the rushing pie until he’s ready to assume the featured role.  However, the more elite the younger rusher is, the more likely he is to take away market share from the older rusher, despite being further behind in his development (think Trent Richardson with Mark Ingram, or T.J. Yeldon with Eddie Lacy).  Then, when it’s his turn to take the lead – he’ll concede much less of the market to the younger back behind him, unless that guy is even more elite (again, Richardson’s Market Share wasn’t dinged all that badly by Lacy).  Basically, the really talented guys just claim their share.

Running quarterbacks are different.  That’s a situation where market share of one position is being stolen by another position because of offensive scheme.  Virginia Tech ran the read option with Thomas and Wilson.  I’m certainly no x’s & o’s guru but the way I understand it, whether or not a running back gets the ball when a team is running the read option is much more a function of tactic than it is talent.  If the defensive end plays the running back, the QB keeps the ball.  If the DE plays the QB, the RB gets the ball.  It becomes a question of the expected success of the QB and RB running options on each play, not a question of whether the QB or the RB is the more talented runner.  I think talent is part of the decision when deciding which running back to put on the field when you need to get yards and TDs to win the game.  So, I think it does make sense to look at rushing market share relative to all runners excluding QBs.  Ultimately, I plan to adjust my DRs to remove the effect of running QBs and I think that will improve the results of my model even further.

Getting back to Wilson – You can slice and dice the hypothetical bump to Wilson’s production any way you want, but there’s no doubt he would look better on my current model if he had a traditional “pocket passer” quarterback.  Taking a more concrete approach, you can look at Wilson’s market share numbers based on RB-only rushing totals as I mentioned above.  Wilson accounted for 78% of the running back yards and 60% of the running back TDs for a RB-Only Rushing DR of .70.  Plugging those numbers into my model gives him an excellent score of 1.06 on my model which translates to a probability of putting up at least one Top 30, 20, or 10 season at 91%, 74%, 70%, respectively. This isn’t exactly fair, because I’m comparing his QB-adjusted numbers to everyone else’s non-adjusted numbers, but I think it’s safe to say Wilson looks much closer to the solid dynasty prospect he’s billed as once we take out the Logan Thomas impact.

Finally, we look to the RB Similarity Score App for some clues.  The beauty of the app is that you can really easily customize the data to remove low-usage weeks for any player.  As we know with Wilson, after fumbling early he was used very sparingly until later in the season.  It doesn’t make a whole lot of sense to include those early weeks since it makes his carries per game number look very low, and brings in some comparable players who were just part-timers in their offenses.  Looking at just the three games in which he received double-digit carries we get this nice looking 2013 projected points range:

2013 Standard PPR
Low 4.2 5.9
Median 10.4 12.2
High 12.1 15.2

There’s still one name that comes up three times in Wilson’s customized comp set that scares the crap out of me.  He’s a first round pick that many view as a bust.  He never even produced a top 20 RB season despite being on a high powered offense…I’m looking at you, Laurence Maroney.

Andre Brown – Taking my model at face value, Brown doesn’t look all that great.  Because draft slot helps explain a meaningful part of running back success, Brown’s selection at the 129th overall spot hurts him a bit (the third round generally seems to be a dropoff for likelihood of fantasy success).  Brown’s 110 Speed Score is good, but that only accounts for a small portion of my model score, so it doesn’t give Brown much of a bump.   On the surface his production is just ok.  A rushing DR of .49 isn’t bad, but it isn’t good either.

The irony is, Brown was also hurt by the Curse of the Running QB during his senior season at NC State.  While Russell Wilson didn’t take quite the chunk out of the 2008 NC State rushing market that Logan Thomas did in David Wilson’s season, he definitely pilfered from the running backs.  @DangeRuss scampered for 23% of the rushing yards and plunged for 29% of the rushing TDs for a Rushing DR of .26.  If we renormalize Brown’s stats to just the RB rushing market, he jumps to 59% of the yards and 70% of the touchdowns for a Rushing DR of .65.  That makes his model score somewhat more intriguing at 0.27, but it makes his Upside Score a lot more intriguing at 1.00, which is about even with David Wilson’s.  I think we saw that upside translate when he got his opportunity after Bradshaw’s injury last season.  In the three games where he received double-digit carries in 2012 he averaged 82.6 yards per game (5.4 YPC) and 1.3 TDs per game.

Again, using the RB Similarity Score App gives us some pretty nice projections for Andre Brown.  It actually doesn’t matter whether you include or exclude his sub 10 carry weeks – his TD rate gives a great comp set.  But to try to do an apples-to-apples comparison with David Wilson, here are his projections using his three double-digit carry games:

2013 Standard PPR
Low 8.9 10
Median 13.6 15.5
High 17.5 20.6

Brown has the goods.  But as we all know, he just can’t stay healthy.  He broke his foot twice in college, ruptured his achilles tendon in his rookie preseason, followed that up with a case of turf toe, got released from the Giants, and bounced around to something like five different teams before finally being resigned to the Giants in 2011.  When he broke out (pun intended) in 2012 he managed to miss week 6 with a concussion and of course he ended the season on I.R. with a broken fibula suffered in week 12.  As amorphous and loaded a term as “injury prone” can be, I think Brown probably qualifies.

That said, you’re getting a pretty good discount on Brown in dynasty startups.  According to Dynasty League Football’s ADP rankings Wilson is going about 24th overall and Brown is going 101st.  Brown should be going much later because he’s 27 while Wilson is only 22, but the amount of value Brown could deliver for the 101st pick is a lot greater (at least in the near term) than what Wilson could probably deliver at the 24th.  Clearly Wilson is the better long-term dynasty asset.  But Brown could be a great short term play requiring little investment.

Stay tuned for my follow-up piece on what we might expect from Tom Coughlin from a usage perspective this season.

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